Dear all,
I am struggling with the calculation of standard error of the coefficient in
Binary logistic regression analysis.
I built a binary logsitic regression model as follows and got confused since
the calculation of standard error of coefficients of X1, X2 and X3 are not
the same as the Linear regression.
> fit4 <-glm(Y~X1+X2+X3,data=d4,family=binomial("logit"))
Warning message:
In glm.fit(x = X, y = Y, weights = weights, start = start, etastart =
etastart, :
fitted probabilities numerically 0 or 1 occurred
> summary(fit4)
Call:
glm(formula = Y ~ X1 + X2 + X3, family = binomial("logit"), data = d4)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.641483e+00 -8.421161e-05 0.000000e+00 1.349398e-03 1.417550e+00
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -10.1534523 10.8397717 -0.93669 0.348921
X1 0.3312469 0.3007324 1.10147 0.270693
X2 0.1808757 0.1069222 1.69166 0.090711 .
X3 5.0874665 5.0820163 1.00107 0.316792
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 91.4954278 on 65 degrees of freedom
Residual deviance: 5.8129055 on 62 degrees of freedom
AIC: 13.812906
Number of Fisher Scoring iterations: 12
Could somebody suggest the calculation of standard error of X1, X2 and X3 in
the output of my model, please?
Any suggestions will be really appreciated.
Kind Regards
Bessy
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